





'''simple docstring'''


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a__			:					List[str]       						=						16
a__			:					int       						=						32
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Accelerator			,	SCREAMING_SNAKE_CASE_ :			int = 16 )							->     int:

       """simple docstring"""


       UpperCAmelCase  =						AutoTokenizer.from_pretrained('''bert-base-cased''' )
       UpperCAmelCase  =						load_dataset('''glue'''			,	'''mrpc''' )

       def tokenize_function(SCREAMING_SNAKE_CASE_ :			Union[str, Any] ):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase  =						tokenizer(examples['''sentence1''']			,	examples['''sentence2''']			,	truncation=SCREAMING_SNAKE_CASE_			,	max_length=SCREAMING_SNAKE_CASE_ )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCAmelCase  =						datasets.map(
                  SCREAMING_SNAKE_CASE_			,	batched=SCREAMING_SNAKE_CASE_			,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,	)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCAmelCase  =						tokenized_datasets.rename_column('''label'''			,	'''labels''' )

       def collate_fn(SCREAMING_SNAKE_CASE_ :			str ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase  =						128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase  =						16
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase  =						8
              else:
                     UpperCAmelCase  =						None

              return tokenizer.pad(
                  SCREAMING_SNAKE_CASE_			,	padding='''longest'''			,	max_length=SCREAMING_SNAKE_CASE_			,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_			,	return_tensors='''pt'''			,	)

       # Instantiate dataloaders.
       UpperCAmelCase  =						DataLoader(
           tokenized_datasets['''train''']			,	shuffle=SCREAMING_SNAKE_CASE_			,	collate_fn=SCREAMING_SNAKE_CASE_			,	batch_size=SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						DataLoader(
           tokenized_datasets['''validation''']			,	shuffle=SCREAMING_SNAKE_CASE_			,	collate_fn=SCREAMING_SNAKE_CASE_			,	batch_size=SCREAMING_SNAKE_CASE_ )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    a__			:					Optional[Any]       						=						mocked_dataloaders  # noqa: F811
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			Any )							->     Any:

       """simple docstring"""


       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,	SCREAMING_SNAKE_CASE_ ) == "1":
              UpperCAmelCase  =						2
       # Initialize accelerator
       UpperCAmelCase  =						Accelerator(cpu=args.cpu			,	mixed_precision=args.mixed_precision )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase  =						config['''lr''']
       UpperCAmelCase  =						int(config['''num_epochs'''] )
       UpperCAmelCase  =						int(config['''seed'''] )
       UpperCAmelCase  =						int(config['''batch_size'''] )

       UpperCAmelCase  =						evaluate.load('''glue'''			,	'''mrpc''' )

       # If the batch size is too big we use gradient accumulation
       UpperCAmelCase  =						1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCAmelCase  =						batch_size // MAX_GPU_BATCH_SIZE
              UpperCAmelCase  =						MAX_GPU_BATCH_SIZE

       set_seed(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase,					UpperCAmelCase  =						get_dataloaders(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase  =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,	return_dict=SCREAMING_SNAKE_CASE_ )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCAmelCase  =						model.to(accelerator.device )

       # Instantiate optimizer
       UpperCAmelCase  =						AdamW(params=model.parameters()			,	lr=SCREAMING_SNAKE_CASE_ )

       # Instantiate scheduler
       UpperCAmelCase  =						get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE_			,	num_warmup_steps=100			,	num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) // gradient_accumulation_steps			,	)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						accelerator.prepare(
           SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE_ ):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device )
                     UpperCAmelCase  =						model(**SCREAMING_SNAKE_CASE_ )
                     UpperCAmelCase  =						outputs.loss
                     UpperCAmelCase  =						loss / gradient_accumulation_steps
                     accelerator.backward(SCREAMING_SNAKE_CASE_ )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              UpperCAmelCase  =						0
              for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device )
                     with torch.no_grad():
                            UpperCAmelCase  =						model(**SCREAMING_SNAKE_CASE_ )
                     UpperCAmelCase  =						outputs.logits.argmax(dim=-1 )
                     UpperCAmelCase,					UpperCAmelCase  =						accelerator.gather((predictions, batch['''labels''']) )
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(SCREAMING_SNAKE_CASE_ ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   UpperCAmelCase  =						predictions[: len(eval_dataloader.dataset ) - samples_seen]
                                   UpperCAmelCase  =						references[: len(eval_dataloader.dataset ) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=SCREAMING_SNAKE_CASE_			,	references=SCREAMING_SNAKE_CASE_			,	)

              UpperCAmelCase  =						metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:"			,	SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     Optional[int]:

       """simple docstring"""


       UpperCAmelCase  =						argparse.ArgumentParser(description='''Simple example of training script.''' )
       parser.add_argument(
           '''--mixed_precision'''			,	type=SCREAMING_SNAKE_CASE_			,	default=SCREAMING_SNAKE_CASE_			,	choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,	help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.'''			,	)
       parser.add_argument('''--cpu'''			,	action='''store_true'''			,	help='''If passed, will train on the CPU.''' )
       UpperCAmelCase  =						parser.parse_args()
       UpperCAmelCase  =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
    main()








"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				__UpperCamelCase         =		DiTPipeline
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				__UpperCamelCase         =		PipelineTesterMixin.required_optional_params - {
				    "latents",
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				__UpperCamelCase         =		False





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							torch.manual_seed(0					)
							lowerCamelCase      =   TransformeraDModel(
							    sample_size=16						,					num_layers=2						,					patch_size=4						,					attention_head_dim=8						,					num_attention_heads=2						,					in_channels=4						,					out_channels=8						,					attention_bias=_a						,					activation_fn="""gelu-approximate"""						,					num_embeds_ada_norm=1_000						,					norm_type="""ada_norm_zero"""						,					norm_elementwise_affine=_a						,					)
							lowerCamelCase      =   AutoencoderKL()
							lowerCamelCase      =   DDIMScheduler()
							lowerCamelCase      =   {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
							return components





				def   _lowerCAmelCase							(			self						,					_a						,					_a=0					):





							"""simple docstring"""
							if str(_a					).startswith("""mps"""					):
										lowerCamelCase      =   torch.manual_seed(_a					)
							else:
										lowerCamelCase      =   torch.Generator(device=_a					).manual_seed(_a					)
							lowerCamelCase      =   {
							    """class_labels""": [1],
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   """cpu"""

							lowerCamelCase      =   self.get_dummy_components()
							lowerCamelCase      =   self.pipeline_class(**_a					)
							pipe.to(_a					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs(_a					)
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape						,					(1, 16, 16, 3)					)
							lowerCamelCase      =   np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]					)
							lowerCamelCase      =   np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(_a						,					1e-3					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_inference_batch_single_identical(relax_max_difference=_a						,					expected_max_diff=1e-3					)





				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()						,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,					)
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






@require_torch_gpu
@slow
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   torch.manual_seed(0					)

							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella""", """white shark""", """white wolf"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=40						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'					)
										assert np.abs((expected_image - image).max()					) < 1e-2





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
							lowerCamelCase      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=25						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    f'/dit/{word}_512.npy'					)

										assert np.abs((expected_image - image).max()					) < 1e-1



